%0 Journal Article
%J Physics in Medicine and Biology
%D 2012
%T Computed Tomography Perfusion Imaging Denoising Using Gaussian Process Regression
%A Fan Zhu
%A Carpenter, Trevor
%A Rodríguez, David
%A Malcolm Atkinson
%A Wardlaw, Joanna
%X Objective: Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, Computed Tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to radiation of the patient. As a consequence, the developments of methods for improving the CNR are valuable. Methods: The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR), which takes advantage of the temporal information, to reduce the noise level. Results: Over the entire image, GPR gains a 99% CNR improvement over the raw images and also improves the quality of haemodynamic maps allowing a better identification of edges and detailed information. At the level of individual voxel, GPR provides a stable baseline, helps us to identify key parameters from tissue time- concentration curves and reduces the oscillations in the curve. Conclusion: GPR is superior to the comparable techniques used in this study.
%B Physics in Medicine and Biology
%> http://research.nesc.ac.uk/files/CT%20Denoising%20by%20Gaussian%20Process.pdf
%0 Conference Paper
%B OHBM 2012
%D 2012
%T A databank, rather than statistical, model of normal ageing brain structure to indicate pathology
%A Dickie, David Alexander
%A Dominic Job
%A Rodríguez, David
%A Shenkin, Susan
%A Wardlaw, Joanna
%B OHBM 2012
%8 10/06/2012
%G eng
%U http://ww4.aievolution.com/hbm1201/index.cfm?do=abs.viewAbs&abs=5102
%0 Journal Article
%J Computer Methods and Programs in Biomedicine
%D 2012
%T Parallel perfusion imaging processing using GPGPU
%A Fan Zhu
%A Rodríguez, David
%A Carpenter, Trevor
%A Malcolm Atkinson
%A Wardlaw, Joanna
%K Deconvolution
%K GPGPU
%K Local AIF
%K Parallelization
%K Perfusion Imaging
%X Background and purpose The objective of brain perfusion quantification is to generate parametric maps of relevant hemodynamic quantities such as cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) that can be used in diagnosis of acute stroke. These calculations involve deconvolution operations that can be very computationally expensive when using local Arterial Input Functions (AIF). As time is vitally important in the case of acute stroke, reducing the analysis time will reduce the number of brain cells damaged and increase the potential for recovery. Methods GPUs originated as graphics generation dedicated co-processors, but modern GPUs have evolved to become a more general processor capable of executing scientific computations. It provides a highly parallel computing environment due to its large number of computing cores and constitutes an affordable high performance computing method. In this paper, we will present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose Graphics Processor Units) using the CUDA programming model. We present the serial and parallel implementations of such algorithms and the evaluation of the performance gains using GPUs. Results Our method has gained a 5.56 and 3.75 speedup for CT and MR images respectively. Conclusions It seems that using GPGPU is a desirable approach in perfusion imaging analysis, which does not harm the quality of cerebral hemodynamic maps but delivers results faster than the traditional computation.
%B Computer Methods and Programs in Biomedicine
%P -
%8 2012
%G eng
%U http://www.sciencedirect.com/science/article/pii/S0169260712001587
%R 10.1016/j.cmpb.2012.06.004
%> http://research.nesc.ac.uk/files/CMPB-D-11-00360R1.pdf
%0 Conference Paper
%B Healthcare Informatics, Imaging, and Systems Biology (HISB)
%D 2011
%T A Parallel Deconvolution Algorithm in Perfusion Imaging
%A Zhu, Fan.
%A Rodríguez, David
%A Carpenter, Trevor
%A Malcolm Atkinson
%A Wardlaw, Joanna
%K Deconvolution
%K GPGPU
%K Parallelization
%K Perfusion Imaging
%X In this paper, we will present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose Graphics Processor Units) using the CUDA programming model. GPUs originated as graphics generation dedicated co-processors, but the modern GPUs have evolved to become a more general processor capable of executing scientific computations. It provides a highly parallel computing environment due to its huge number of computing cores and constitutes an affordable high performance computing method. The objective of brain perfusion quantification is to generate parametric maps of relevant haemodynamic quantities such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV) and Mean Transit Time (MTT) that can be used in diagnosis of conditions such as stroke or brain tumors. These calculations involve deconvolution operations that in the case of using local Arterial Input Functions (AIF) can be very expensive computationally. We present the serial and parallel implementations of such algorithm and the evaluation of the performance gains using GPUs.
%B Healthcare Informatics, Imaging, and Systems Biology (HISB)
%C San Jose, California
%8 26/07/2011
%@ 978-1-4577-0325-6
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6061411&tag=1
%R 10.1109/HISB.2011.6
%> http://research.nesc.ac.uk/files/PID1888641.pdf
%0 Conference Paper
%B All Hands Meeting 2011, York
%D 2011
%T RapidBrain: Developing a Portal for Brain Research Imaging
%A Kenton D'Mellow
%A Rodríguez, David
%A Carpenter, Trevor
%A Jos Koetsier
%A Dominic Job
%A van Hemert, Jano
%A Wardlaw, Joanna
%A Fan Zhu
%X Brain imaging researchers execute complex multistep workflows in their computational analysis. Those workflows often include applications that have very different user interfaces and sometimes use different data formats. A good example is the brain perfusion quantification workflow used at the BRIC (Brain Research Imaging Centre) in Edinburgh. Rapid provides an easy method for creating portlets for computational jobs, and at the same it is extensible. We have exploited this extensibility with additions that stretch the functionality beyond the original limits. These changes can be used by other projects to create their own portals, but it should be noted that the development of such portals involve a greater effort than the required in the regular use of Rapid for creating portlets. In our case it has been used to provide a user-friendly interface for perfusion analysis that covers from volume
%B All Hands Meeting 2011, York
%C York
%G eng